Three-quarters of organizations now run AI in production environments. Ninety-nine percent reported at least one attack on their AI systems within the past year. The data flowing into and out of these models, training datasets, fine-tuning inputs, prompt histories, and generated outputs, represents a category of data exposure that most security programs were never built to monitor. When a large language model is fine-tuned on customer records, internal strategy documents, or proprietary code, the question of who can access what the model learned becomes urgent.

AI pipelines create data exfiltration paths that blend into legitimate business operations. A model query that returns sensitive information is not a traditional data breach, but the impact can be identical. Addressing this requires coordination across DSPM, DLP, SaaS security, cloud security, and AI data governance platforms to build visibility into what data feeds AI systems, what those systems can reveal through inference, and whether access controls exist at each stage of the pipeline. The data security perimeter has expanded, and the tools designed to protect structured databases and file shares are not sufficient for the unstructured, dynamic data flows that AI depends on.

Topics include:

• Gaining visibility into what sensitive data enters AI training pipelines and inference endpoints
• Extending data loss prevention and SaaS security strategies to cover AI-specific exfiltration vectors
• Building governance frameworks for AI data flows across development, staging, and production

Discover how organizations are closing the data security gap created by enterprise AI adoption before it becomes their next breach headline.